PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry. (arXiv:2003.07723v3 [cs.CL] UPDATED)
(2 min)
Most approaches to emotion analysis of social media, literature, news, and
other domains focus exclusively on basic emotion categories as defined by Ekman
or Plutchik. However, art (such as literature) enables engagement in a broader
range of more complex and subtle emotions. These have been shown to also
include mixed emotional responses. We consider emotions in poetry as they are
elicited in the reader, rather than what is expressed in the text or intended
by the author. Thus, we conceptualize a set of aesthetic emotions that are
predictive of aesthetic appreciation in the reader, and allow the annotation of
multiple labels per line to capture mixed emotions within their context. We
evaluate this novel setting in an annotation experiment both with carefully
trained experts and via crowdsourcing. Our annotation with experts leads to an
acceptable agreement of kappa = .70, resulting in a consistent dataset for
future large scale analysis. Finally, we conduct first emotion classification
experiments based on BERT, showing that identifying aesthetic emotions is
challenging in our data, with up to .52 F1-micro on the German subset. Data and
resources are available at https://github.com/tnhaider/poetry-emotion